2. What you really need to know

The backbone of Julearn is the function run_cross_validation(), which allows you to do all the magic. All important information needed to estimate your machine learning workflow’s performance goes into this function, specified via its parameters.

But why is basically everything based on one cross_validation function? Well, because doing proper cross-validation is of utmost importance in machine learning and it is not as easy as it might seem at first glance. If you want to understand why, reading the sub-chapter Cross-validation: evaluating estimator performance ist a good starting point.

Once you are familiar with the basics of cross-validation, you can follow along the other sub-chapters to learn how to setup a basic workflow using Julearn’s run_cross_validation(). There you can find out more about the required data, building a basic pipeline and how to evaluate your model’s performance.

• 2.1. Why cross validation?
  • Cross-validation - The fundamentals
  • The essence of run_cross_validation()
• 2.2. Data
  • Data input to run_cross_validation()
  • Giving types to features
• 2.3. Model Building
  • Pipeline specification in run_cross_validation()
  • Feature preprocessing
  • Pipeline specification made easy with the PipelineCreator
  • Setting hyperparameters
  • Selective preprocessing using feature types
• 2.4. Model evaluation
  • The output of run_cross_validation()
  • Cross-validation splitters
  • Scoring metrics
• 2.5. Model comparison
  • Statistical comparisons
  • Score visualizations

If you are just interested in seeing all parameters of run_cross_validation(), click on the function link to have a look at all its parameters in the Reference.

If you are already familiar with how to set up a basic workflow using Julearn and want to do more fancy stuff, go to Selected deeper topics.